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In the study presented, different hybrid model approaches are proposed for reservoir inflow modeling from the meteorological data (monthly precipitation, one-month-ahead precipitation and monthly mean temperature data) by the combined use of discrete wavelet transform (DWT) and different black box techniques. Multiple linear regression (MLR), feed forward neural networks (FFNN) and least square support vector machines (LSSVM) were considered as the black box methods. In the modeling strategy, meteorological input data were decomposed into wavelet sub-time series at three resolution levels and ineffective sub-time series were eliminated by Mallows’ Cp based all possible regression method. As a result of all possible regression analyses, 2-months mode of time series of monthly temperature (D1_Tt), 8-months mode of time series (D3_Tt) of monthly temperature and approximation mode of time series (A3_Tt) of monthly temperature were eliminated. Remained effective sub-time series were used as the inputs of MLR, FFNN and LSSVM. When the performances of the training and testing periods were compared, it was observed that the DWTFFNN conjunction model has better results in terms of mean square errors (MSE) and determination coefficients (R2) statistics. The discrete wavelet transform approach also increased the accuracy of multiple linear regression and least squares support vector machines.
The invasive capability is fundamental in determining the malignancy of a solid tumor. In particular, tumor invasion fronts are characterized by different morphologies, which result both from cell-based processes (such as cell elasticity, adhesive properties and motility) and from subcellular molecular dynamics (such as growth factor internalization, ECM protein digestion and MMP secretion). Of particular relevance is the development of tumors with unstable fingered morphologies: they are in fact more aggressive and hard to be treated than smoother ones as, even if their invasive depth is limited, they are difficult to be surgically removed. The phenomenon of malignant fingering has been reproduced with several mathematical approaches. In this respect, we here present a qualitative comparison between the results obtained by an individual cell-based model (an extended version of the cellular Potts model) and by a measure-based theoretic method. In particular, we show that in both cases a fundamental role in finger extension is played by intercellular adhesive forces and taxis-like migration.
Given its importance in water resources management, particularly in terms of minimizing flood or drought hazards, precipitation forecasting has seen a wide variety of approaches tested. As monthly precipitation time series have nonlinear features and multiple time scales, wavelet, seasonal auto regressive integrated moving average (SARIMA) and hybrid artificial neural network (ANN) methods were tested for their ability to accurately predict monthly precipitation. A 40-year (1970–2009) precipitation time series from Iran’s Nahavand meteorological station (34°12’N lat., 48°22’E long.) was decomposed into one low frequency subseries and several high frequency sub-series by wavelet transform. The low frequency sub-series were predicted with a SARIMA model, while high frequency subseries were predicted with an ANN. Finally, the predicted subseries were reconstructed to predict the precipitation of future single months. Comparing model-generated values with observed data, the wavelet-SARIMA-ANN model was seen to outperform wavelet-ANN and wavelet-SARIMA models in terms of precipitation forecasting accuracy.
The purpose of this article is to present the main directions of changes in the Estonian health care system following the transformation of the national economy and the accession of Estonia to the European Union. Special attention has been paid to the ways of sourcing, and the collection and redistribution of financial resources allocated to health care in different periods of the transformation. The initial changes introduced far-reaching decentralization of the health system, while further reforms led to his re-centralization. The intensity of the re-centralization of finance and health management processes was accelerated after 2008, when the impact of the global financial crisis on the condition of the economy of Estonia was significant. As a result of the introduced changes, Bismarck’s mixed system - a hybrid system - has been formed.
Non-linear, dynamic, non-stationary properties characterize objects of the iron ore beneficiation line. Therefore, for their approximation, it is advisable to use models of the Hammerstein class. As a result of comparing the three models of Hammerstein: simple, parallel and recursive-parallel, it was shown that the best result for identifying the considered processes of magnetic beneficiation of iron ore by the minimum error criterion was obtained using the Hammerstein recursive-parallel model. Hence, it is recommended for the identification of beneficiation production objects.
A hybrid ANN-FE solution is presented as a result of two level analysis of soils: a level of a laboratory sample and a level of engineering geotechnical problem. Engineering properties of soils (sands) are represented directly in the form of ANN (this is in contrast with our former paper where ANN approximated constitutive relationships). Initially the ANN is trained with Duncan formula (Duncan and Chang ), then it is re-trained (calibrated) with some available experimental data, specific for the soil considered. The obtained approximation of the constitutive parameters is used directly in finite element method at the level of a single element at the scale of the laboratory sample to check the correct representation of the laboratory test. Then, the finite element that was successfully tested at the level of laboratory sample is used at the macro level to solve engineering problems involving the soil for which it was calibrated.
Hybrid imaging represents a combination of two different imaging techniques resulting in a single image that contains all the information provided by the two investigations. Hybrid imaging tends to improve the accuracy of the diagnosis in many diseases. Coronary computed tomography angiography (CCTA) has unquestionable abilities in highlighting coronary artery diseases (CAD). Cardiac magnetic resonance imaging (MRI) also has a powerful predictive role in assessing the functionality of the myocardial tissue.
The aim of the study is to develop new imaging markers for a complex evaluation of myocardial viability (MV) after an acute myocardial infarction (AMI), using hybrid technology.
Material and methods: This study will enroll 100 patients at one month after an AMI. CCTA, MRI, 3D echocardiography, and blood tests will be performed in all patients. All the acquisitions will be processed using a supercomputer, and MV and other parameters will be assessed on hybrid images. A secondary objective will be to correlate the level of inflammatory markers with the outcome of patients, left ventricular function, ischemic time, and the rate of major adverse cardiovascular events.